Our approach to disentangling environmental factors of variation and object attributes in tactile sensing is based on work in computer vision where disentanglement is considered important to improve the generalizability of machine learning models, for example to aid in learning of robust object representations for object classification under novel scenarios [1]}. Inspired from this, here we propose a supervised convolutional neural network model that learns to disentangle the components of sensor deformation caused by the contacted object geometry from those due to motion-induced shear. The models were trained using paired data from discrete (tapping) and continuous (sliding) touch, with an image-to-image `Disentangled UnshearNet' model architecture that outputs unsheared tactile images from sheared images (Fig. REF ). Our validation: (1) quantifies that the unsheared images from continuous motion match their paired tactile images from discrete taps (Table REF ); (2) demonstrates that local contact geometry reconstructed from unsheared images using Voronoi match their paired tapping counterparts over several stimuli shapes (Fig. REF , top row); (3) infers pose from the sheared tactile images by re-using a convolutional neural network model trained on discrete (tapping) data, which was then applied to controlling robust sliding exploration around several planar objects (Fig. REF , middle row); and (4) combines the two previous steps to attain faithful full-object reconstruction of several planar shapes (Fig. REF , bottom row).
